Automated Detection of Solar Radio Bursts Using a Statistical Method

Abstract

adio bursts from the solar corona can provide clues to forecast space-weather hazards. After recent technology advancements, regular monitoring of radio bursts has increased and large observational datasets are produced. Hence, manual identification and classification of them is a challenging task. In this article, we describe an algorithm to automatically identify radio bursts from dynamic solar radio spectrograms using a novel statistical method. We use e-CALLISTO (Compound Astronomical Low Cost Low Frequency Instrument for Spectroscopy and Transportable Observatory) radio spectrometer data obtained at Gauribidanur Observatory near Bangalore in India during 2013 – 2014. We have studied the classifier performance using the receiver operating characteristics. Further, we analyze type III bursts observed in the year 2014 and find that 75% of the observed bursts were below 200 MHz. Our analysis shows that the positions of flare sites, which are associated with the type III bursts with upper frequency cutoff ≳200 MHz originate close to the solar disk center.